The main source of information on future climate conditions are global circulation models (GCMs). While the various GCMs agree on an increase of surface temperature, the predictions for precipitation exhibit high spread among the models, especially in shorter-than-daily temporal resolution. This paper presents a method to predict regional distributions of the hourly rainfall depth based on daily mean sea level pressure and temperature data. It is an indirect downscaling method avoiding uncertain precipitation data from the GCM. It is based on a fuzzy logic classification of atmospheric circulation patterns (CPs) that is further subdivided by means of the average daily temperature. The observed empirical distributions at 30 rain gauges to each CP-temperature class are assumed as constant and used for projections of the hourly precipitation sums in the future. The method was applied to the CP-temperature sequence derived from the 20th-century run and the scenario A1B run of ECHAM5. For the study region in southwestern Germany ECHAM5 predicts that the summers will become progressively drier. Nevertheless, the frequency of the highest hourly precipitation sums will increase. According to the predictions, estival water stress and the risk of extreme hourly precipitation will both increase simultaneously during the next decades. However, the results are yet to be confirmed by further investigation based on other GCMs.